Multicorn Foreign Data Wrapper vs plpython

Multicorn technology allows you to develop FDWs in Python, which is much easier and faster than creating FDWs in C. However, there is a downside, Multicorn FDWs work well with primitive WHERE conditions, but more complex cases cause difficulties, which I will talk about. Cases will be considered on the example of my Multicorn FDW for getting OpenStreetMap data. I will also show examples of using the same code in Multicorn FDW and plpython functions, including performance comparison. In conclusion, I will share my findings on when it is better to use plpython, and when Multicorn FDW is more preferable.

Intel Technologies for PostgreSQL

In this presentation, we'll discuss Intel products and solutions intended for the Data Platform Group segment, such as Xeon 3rd Gen (4S Cooper Lake) server CPUs, PMEm 200 Series RAM and FPGA.

Building automatic adviser and performance tuning tools in PostgreSQL

PostgreSQL is a mature and robust RDBMS since it has 30 years of history. Over the year, its query optimizer has been enhanced and usually produces good query plans.

However, can it always come up with good query plans? The optimization process has to use some assumptions to produce plans fast enough. Some of those assumptions are relatively easy to check (e.g. statistics are up-to-date), some harder (e.g. correct indexes are created), and some nearly impossible (e.g. making sure that the statistic samples are representative enough even for skewed data repartition). For now, given those various caveats, DBA sometimes can't always realize easily that they miss a chance to get a meaningful performance improvement.

To help DBA to get a truly good query plan, we'll present below some tools that can help to fix some of those problems by providing a missing index adviser, looking for extended statistics to create, and row estimation error correction information to get appropriate join orders with join methods automatically.

• pg_qualstats: provides a new index and extended statistics suggestions to gather many predicate statistics on the production workload.
• pg_plan_advsr: provides alternative good query plans automatically to analyze iterative query executions information to fix estimation rows error.

In this talk, we will explain how those tools work under the hood and see what can be done, how they can work together. Also, we will mention what other tools also exist for related problems. Therefore, it will be useful for DBA who are interested in improving query performance or want to check whether current settings of indexes and statistics are adequate.

Modern DWH with open-source PostgreSQL

PostgreSQL has a long history in DWH. Netezza, Redshift, and Greenplum have turned specific PostgreSQL releases into DWH solutions. Nowadays, with the trends in PostgreSQL towards performance improvements (better partitioning, better statistics, JIT Compilation, etc.) and advanced PostgreSQL extensions, like the Swarm64 Data Accelerator, you can create a forward-looking, no lock-in, versatile, and reliable DWH. This talk will cover the PostgreSQL and DWH trends and touch on key arguments for choosing open source PostgreSQL for DWH.